Overview

Dataset statistics

Number of variables34
Number of observations13
Missing cells12
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 KiB
Average record size in memory378.0 B

Variable types

Text2
Numeric27
Categorical5

Alerts

title has constant value ""Constant
data__Curfew and Loitering Law Violations has constant value ""Constant
data__Drunkenness has constant value ""Constant
data__Human Trafficking - Involuntary Servitude has constant value ""Constant
data__Suspicion has constant value ""Constant
data__Aggravated Assault is highly overall correlated with data__All Other Offenses (Except Traffic) and 23 other fieldsHigh correlation
data__All Other Offenses (Except Traffic) is highly overall correlated with data__Aggravated Assault and 23 other fieldsHigh correlation
data__Arson is highly overall correlated with data__Aggravated Assault and 23 other fieldsHigh correlation
data__Burglary is highly overall correlated with data__Aggravated Assault and 21 other fieldsHigh correlation
data__Disorderly Conduct is highly overall correlated with data__Aggravated Assault and 22 other fieldsHigh correlation
data__Driving Under the Influence is highly overall correlated with data__Aggravated Assault and 23 other fieldsHigh correlation
data__Drug Abuse Violations - Grand Total is highly overall correlated with data__Arson and 1 other fieldsHigh correlation
data__Embezzlement is highly overall correlated with data__Aggravated Assault and 22 other fieldsHigh correlation
data__Forgery and Counterfeiting is highly overall correlated with data__Aggravated Assault and 23 other fieldsHigh correlation
data__Fraud is highly overall correlated with data__Aggravated Assault and 23 other fieldsHigh correlation
data__Gambling - Total is highly overall correlated with data__Aggravated Assault and 22 other fieldsHigh correlation
data__Human Trafficking - Commercial Sex Acts is highly overall correlated with data__Aggravated Assault and 8 other fieldsHigh correlation
data__Larceny - Theft is highly overall correlated with data__Aggravated Assault and 20 other fieldsHigh correlation
data__Liquor Laws is highly overall correlated with data__Aggravated Assault and 22 other fieldsHigh correlation
data__Manslaughter by Negligence is highly overall correlated with data__Aggravated Assault and 15 other fieldsHigh correlation
data__Motor Vehicle Theft is highly overall correlated with data__Aggravated Assault and 20 other fieldsHigh correlation
data__Murder and Nonnegligent Manslaughter is highly overall correlated with data__Motor Vehicle TheftHigh correlation
data__Offenses Against the Family and Children is highly overall correlated with data__Aggravated Assault and 15 other fieldsHigh correlation
data__Prostitution and Commercialized Vice is highly overall correlated with data__Aggravated Assault and 19 other fieldsHigh correlation
data__Robbery is highly overall correlated with data__Aggravated Assault and 22 other fieldsHigh correlation
data__Sex Offenses (Except Rape, and Prostitution and Commercialized Vice) is highly overall correlated with data__Aggravated Assault and 22 other fieldsHigh correlation
data__Simple Assault is highly overall correlated with data__Aggravated Assault and 22 other fieldsHigh correlation
data__Stolen Property: Buying, Receiving, Possessing is highly overall correlated with data__Aggravated Assault and 20 other fieldsHigh correlation
data__Vagrancy is highly overall correlated with data__Aggravated Assault and 22 other fieldsHigh correlation
data__Vandalism is highly overall correlated with data__Aggravated Assault and 22 other fieldsHigh correlation
data__Weapons: Carrying, Possessing, Etc. is highly overall correlated with data__Aggravated Assault and 22 other fieldsHigh correlation
data__data_year is highly overall correlated with data__Aggravated Assault and 22 other fieldsHigh correlation
data__Human Trafficking - Commercial Sex Acts is highly imbalanced (60.9%)Imbalance
title has 12 (92.3%) missing valuesMissing
keys has unique valuesUnique
data__data_year has unique valuesUnique
data__Aggravated Assault has unique valuesUnique
data__All Other Offenses (Except Traffic) has unique valuesUnique
data__Burglary has unique valuesUnique
data__Disorderly Conduct has unique valuesUnique
data__Driving Under the Influence has unique valuesUnique
data__Drug Abuse Violations - Grand Total has unique valuesUnique
data__Embezzlement has unique valuesUnique
data__Forgery and Counterfeiting has unique valuesUnique
data__Fraud has unique valuesUnique
data__Gambling - Total has unique valuesUnique
data__Larceny - Theft has unique valuesUnique
data__Liquor Laws has unique valuesUnique
data__Motor Vehicle Theft has unique valuesUnique
data__Offenses Against the Family and Children has unique valuesUnique
data__Prostitution and Commercialized Vice has unique valuesUnique
data__Rape has unique valuesUnique
data__Robbery has unique valuesUnique
data__Simple Assault has unique valuesUnique
data__Stolen Property: Buying, Receiving, Possessing has unique valuesUnique
data__Vagrancy has unique valuesUnique
data__Vandalism has unique valuesUnique
data__Weapons: Carrying, Possessing, Etc. has unique valuesUnique
data__Sex Offenses (Except Rape, and Prostitution and Commercialized Vice) has unique valuesUnique

Reproduction

Analysis started2024-03-02 18:53:45.107030
Analysis finished2024-03-02 18:54:37.711235
Duration52.6 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

title
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing12
Missing (%)92.3%
Memory size594.0 B
2024-03-03T00:24:37.779502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters21
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowAll Arrest by Offense
ValueCountFrequency (%)
all 1
25.0%
arrest 1
25.0%
by 1
25.0%
offense 1
25.0%
2024-03-03T00:24:37.929196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3
14.3%
e 3
14.3%
A 2
9.5%
l 2
9.5%
r 2
9.5%
s 2
9.5%
f 2
9.5%
t 1
 
4.8%
b 1
 
4.8%
y 1
 
4.8%
Other values (2) 2
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15
71.4%
Space Separator 3
 
14.3%
Uppercase Letter 3
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3
20.0%
l 2
13.3%
r 2
13.3%
s 2
13.3%
f 2
13.3%
t 1
 
6.7%
b 1
 
6.7%
y 1
 
6.7%
n 1
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
A 2
66.7%
O 1
33.3%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18
85.7%
Common 3
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3
16.7%
A 2
11.1%
l 2
11.1%
r 2
11.1%
s 2
11.1%
f 2
11.1%
t 1
 
5.6%
b 1
 
5.6%
y 1
 
5.6%
O 1
 
5.6%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3
14.3%
e 3
14.3%
A 2
9.5%
l 2
9.5%
r 2
9.5%
s 2
9.5%
f 2
9.5%
t 1
 
4.8%
b 1
 
4.8%
y 1
 
4.8%
Other values (2) 2
9.5%

keys
Text

UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-03-03T00:24:38.117631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length35
Median length26
Mean length19.307692
Min length5

Characters and Unicode

Total characters251
Distinct characters41
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)100.0%

Sample

1st rowAggravated Assault
2nd rowAll Other Offenses (Except Traffic)
3rd rowArson
4th rowBurglary
5th rowCurfew and Loitering Law Violations
ValueCountFrequency (%)
total 2
 
5.7%
2
 
5.7%
and 2
 
5.7%
violations 2
 
5.7%
aggravated 1
 
2.9%
under 1
 
2.9%
fraud 1
 
2.9%
counterfeiting 1
 
2.9%
forgery 1
 
2.9%
embezzlement 1
 
2.9%
Other values (21) 21
60.0%
2024-03-03T00:24:38.306789image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22
 
8.8%
e 21
 
8.4%
n 20
 
8.0%
r 19
 
7.6%
a 15
 
6.0%
t 14
 
5.6%
i 13
 
5.2%
o 12
 
4.8%
l 12
 
4.8%
s 11
 
4.4%
Other values (31) 92
36.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 195
77.7%
Uppercase Letter 30
 
12.0%
Space Separator 22
 
8.8%
Dash Punctuation 2
 
0.8%
Close Punctuation 1
 
0.4%
Open Punctuation 1
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 21
10.8%
n 20
10.3%
r 19
 
9.7%
a 15
 
7.7%
t 14
 
7.2%
i 13
 
6.7%
o 12
 
6.2%
l 12
 
6.2%
s 11
 
5.6%
u 10
 
5.1%
Other values (14) 48
24.6%
Uppercase Letter
ValueCountFrequency (%)
A 5
16.7%
D 4
13.3%
T 3
10.0%
C 3
10.0%
G 2
 
6.7%
V 2
 
6.7%
L 2
 
6.7%
F 2
 
6.7%
E 2
 
6.7%
O 2
 
6.7%
Other values (3) 3
10.0%
Space Separator
ValueCountFrequency (%)
22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 225
89.6%
Common 26
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 21
 
9.3%
n 20
 
8.9%
r 19
 
8.4%
a 15
 
6.7%
t 14
 
6.2%
i 13
 
5.8%
o 12
 
5.3%
l 12
 
5.3%
s 11
 
4.9%
u 10
 
4.4%
Other values (27) 78
34.7%
Common
ValueCountFrequency (%)
22
84.6%
- 2
 
7.7%
) 1
 
3.8%
( 1
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22
 
8.8%
e 21
 
8.4%
n 20
 
8.0%
r 19
 
7.6%
a 15
 
6.0%
t 14
 
5.6%
i 13
 
5.2%
o 12
 
4.8%
l 12
 
4.8%
s 11
 
4.4%
Other values (31) 92
36.7%

data__data_year
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015
Minimum2009
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:38.387088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2009.6
Q12012
median2015
Q32018
95-th percentile2020.4
Maximum2021
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.8944405
Coefficient of variation (CV)0.0019327248
Kurtosis-1.2
Mean2015
Median Absolute Deviation (MAD)3
Skewness0
Sum26195
Variance15.166667
MonotonicityStrictly increasing
2024-03-03T00:24:38.465556image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2009 1
 
7.7%
2010 1
 
7.7%
2011 1
 
7.7%
2012 1
 
7.7%
2013 1
 
7.7%
2014 1
 
7.7%
2015 1
 
7.7%
2016 1
 
7.7%
2017 1
 
7.7%
2018 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
2009 1
7.7%
2010 1
7.7%
2011 1
7.7%
2012 1
7.7%
2013 1
7.7%
2014 1
7.7%
2015 1
7.7%
2016 1
7.7%
2017 1
7.7%
2018 1
7.7%
ValueCountFrequency (%)
2021 1
7.7%
2020 1
7.7%
2019 1
7.7%
2018 1
7.7%
2017 1
7.7%
2016 1
7.7%
2015 1
7.7%
2014 1
7.7%
2013 1
7.7%
2012 1
7.7%

data__Aggravated Assault
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8482.3077
Minimum2528
Maximum10690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:38.527070image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2528
5-th percentile5323.4
Q18146
median8698
Q39759
95-th percentile10630
Maximum10690
Range8162
Interquartile range (IQR)1613

Descriptive statistics

Standard deviation2083.6315
Coefficient of variation (CV)0.24564441
Kurtosis5.6849806
Mean8482.3077
Median Absolute Deviation (MAD)1061
Skewness-2.0362811
Sum110270
Variance4341520.1
MonotonicityNot monotonic
2024-03-03T00:24:38.589835image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10690 1
 
7.7%
10590 1
 
7.7%
9759 1
 
7.7%
10135 1
 
7.7%
9025 1
 
7.7%
8531 1
 
7.7%
8698 1
 
7.7%
8765 1
 
7.7%
8594 1
 
7.7%
8146 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
2528 1
7.7%
7187 1
7.7%
7622 1
7.7%
8146 1
7.7%
8531 1
7.7%
8594 1
7.7%
8698 1
7.7%
8765 1
7.7%
9025 1
7.7%
9759 1
7.7%
ValueCountFrequency (%)
10690 1
7.7%
10590 1
7.7%
10135 1
7.7%
9759 1
7.7%
9025 1
7.7%
8765 1
7.7%
8698 1
7.7%
8594 1
7.7%
8531 1
7.7%
8146 1
7.7%

data__All Other Offenses (Except Traffic)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49690
Minimum12251
Maximum79418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:38.657570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum12251
5-th percentile20286.2
Q142343
median47286
Q368123
95-th percentile76565
Maximum79418
Range67167
Interquartile range (IQR)25780

Descriptive statistics

Standard deviation19402.01
Coefficient of variation (CV)0.39046107
Kurtosis-0.253009
Mean49690
Median Absolute Deviation (MAD)10409
Skewness-0.17539583
Sum645970
Variance3.7643801 × 108
MonotonicityStrictly decreasing
2024-03-03T00:24:38.716981image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
79418 1
 
7.7%
74663 1
 
7.7%
69703 1
 
7.7%
68123 1
 
7.7%
52861 1
 
7.7%
47476 1
 
7.7%
47286 1
 
7.7%
44971 1
 
7.7%
44355 1
 
7.7%
42343 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
12251 1
7.7%
25643 1
7.7%
36877 1
7.7%
42343 1
7.7%
44355 1
7.7%
44971 1
7.7%
47286 1
7.7%
47476 1
7.7%
52861 1
7.7%
68123 1
7.7%
ValueCountFrequency (%)
79418 1
7.7%
74663 1
7.7%
69703 1
7.7%
68123 1
7.7%
52861 1
7.7%
47476 1
7.7%
47286 1
7.7%
44971 1
7.7%
44355 1
7.7%
42343 1
7.7%

data__Arson
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean288.38462
Minimum114
Maximum419
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:38.779961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum114
5-th percentile181.2
Q1242
median286
Q3344
95-th percentile395.6
Maximum419
Range305
Interquartile range (IQR)102

Descriptive statistics

Standard deviation77.97493
Coefficient of variation (CV)0.27038519
Kurtosis1.0899844
Mean288.38462
Median Absolute Deviation (MAD)58
Skewness-0.51119926
Sum3749
Variance6080.0897
MonotonicityNot monotonic
2024-03-03T00:24:38.843775image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
298 2
15.4%
282 2
15.4%
419 1
7.7%
351 1
7.7%
286 1
7.7%
380 1
7.7%
344 1
7.7%
242 1
7.7%
226 1
7.7%
227 1
7.7%
ValueCountFrequency (%)
114 1
7.7%
226 1
7.7%
227 1
7.7%
242 1
7.7%
282 2
15.4%
286 1
7.7%
298 2
15.4%
344 1
7.7%
351 1
7.7%
380 1
7.7%
ValueCountFrequency (%)
419 1
7.7%
380 1
7.7%
351 1
7.7%
344 1
7.7%
298 2
15.4%
286 1
7.7%
282 2
15.4%
242 1
7.7%
227 1
7.7%
226 1
7.7%

data__Burglary
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6388.7692
Minimum1317
Maximum9333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:38.911391image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1317
5-th percentile2962.8
Q14696
median6427
Q38317
95-th percentile9037.8
Maximum9333
Range8016
Interquartile range (IQR)3621

Descriptive statistics

Standard deviation2345.7591
Coefficient of variation (CV)0.36716917
Kurtosis0.065935739
Mean6388.7692
Median Absolute Deviation (MAD)1869
Skewness-0.67562673
Sum83054
Variance5502585.9
MonotonicityNot monotonic
2024-03-03T00:24:39.003229image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
8317 1
 
7.7%
8747 1
 
7.7%
8841 1
 
7.7%
9333 1
 
7.7%
8130 1
 
7.7%
7198 1
 
7.7%
6427 1
 
7.7%
5950 1
 
7.7%
5480 1
 
7.7%
4696 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
1317 1
7.7%
4060 1
7.7%
4558 1
7.7%
4696 1
7.7%
5480 1
7.7%
5950 1
7.7%
6427 1
7.7%
7198 1
7.7%
8130 1
7.7%
8317 1
7.7%
ValueCountFrequency (%)
9333 1
7.7%
8841 1
7.7%
8747 1
7.7%
8317 1
7.7%
8130 1
7.7%
7198 1
7.7%
6427 1
7.7%
5950 1
7.7%
5480 1
7.7%
4696 1
7.7%
Distinct1
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size886.0 B
0
13 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13
100.0%

Length

2024-03-03T00:24:39.110599image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T00:24:39.298791image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13
100.0%

Most occurring characters

ValueCountFrequency (%)
0 13
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13
100.0%

data__Disorderly Conduct
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8136.1538
Minimum701
Maximum15063
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:39.378156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum701
5-th percentile1435.4
Q15271
median7287
Q312904
95-th percentile14606.4
Maximum15063
Range14362
Interquartile range (IQR)7633

Descriptive statistics

Standard deviation4730.7671
Coefficient of variation (CV)0.58145005
Kurtosis-1.2041291
Mean8136.1538
Median Absolute Deviation (MAD)3304
Skewness0.043478741
Sum105770
Variance22380157
MonotonicityStrictly decreasing
2024-03-03T00:24:39.456691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
15063 1
 
7.7%
14302 1
 
7.7%
13176 1
 
7.7%
12904 1
 
7.7%
10341 1
 
7.7%
8731 1
 
7.7%
7287 1
 
7.7%
6498 1
 
7.7%
5588 1
 
7.7%
5271 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
701 1
7.7%
1925 1
7.7%
3983 1
7.7%
5271 1
7.7%
5588 1
7.7%
6498 1
7.7%
7287 1
7.7%
8731 1
7.7%
10341 1
7.7%
12904 1
7.7%
ValueCountFrequency (%)
15063 1
7.7%
14302 1
7.7%
13176 1
7.7%
12904 1
7.7%
10341 1
7.7%
8731 1
7.7%
7287 1
7.7%
6498 1
7.7%
5588 1
7.7%
5271 1
7.7%

data__Driving Under the Influence
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28852.231
Minimum4256
Maximum39217
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:39.519707image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum4256
5-th percentile12672.8
Q127000
median30361
Q334042
95-th percentile38918.8
Maximum39217
Range34961
Interquartile range (IQR)7042

Descriptive statistics

Standard deviation9273.2505
Coefficient of variation (CV)0.32140497
Kurtosis3.607258
Mean28852.231
Median Absolute Deviation (MAD)3681
Skewness-1.6506638
Sum375079
Variance85993175
MonotonicityNot monotonic
2024-03-03T00:24:39.598434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
36694 1
 
7.7%
38720 1
 
7.7%
39217 1
 
7.7%
34042 1
 
7.7%
31174 1
 
7.7%
30546 1
 
7.7%
30361 1
 
7.7%
29984 1
 
7.7%
28533 1
 
7.7%
27000 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
4256 1
7.7%
18284 1
7.7%
26268 1
7.7%
27000 1
7.7%
28533 1
7.7%
29984 1
7.7%
30361 1
7.7%
30546 1
7.7%
31174 1
7.7%
34042 1
7.7%
ValueCountFrequency (%)
39217 1
7.7%
38720 1
7.7%
36694 1
7.7%
34042 1
7.7%
31174 1
7.7%
30546 1
7.7%
30361 1
7.7%
29984 1
7.7%
28533 1
7.7%
27000 1
7.7%

data__Drug Abuse Violations - Grand Total
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53292.154
Minimum4160
Maximum76716
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:39.677474image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum4160
5-th percentile16671.8
Q135288
median64385
Q367322
95-th percentile76351.2
Maximum76716
Range72556
Interquartile range (IQR)32034

Descriptive statistics

Standard deviation22862.065
Coefficient of variation (CV)0.42899495
Kurtosis0.038265374
Mean53292.154
Median Absolute Deviation (MAD)6718
Skewness-1.0861289
Sum692798
Variance5.2267402 × 108
MonotonicityNot monotonic
2024-03-03T00:24:39.788870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
61957 1
 
7.7%
65710 1
 
7.7%
65331 1
 
7.7%
67322 1
 
7.7%
67324 1
 
7.7%
64385 1
 
7.7%
25013 1
 
7.7%
25817 1
 
7.7%
76716 1
 
7.7%
76108 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
4160 1
7.7%
25013 1
7.7%
25817 1
7.7%
35288 1
7.7%
57667 1
7.7%
61957 1
7.7%
64385 1
7.7%
65331 1
7.7%
65710 1
7.7%
67322 1
7.7%
ValueCountFrequency (%)
76716 1
7.7%
76108 1
7.7%
67324 1
7.7%
67322 1
7.7%
65710 1
7.7%
65331 1
7.7%
64385 1
7.7%
61957 1
7.7%
57667 1
7.7%
35288 1
7.7%

data__Drunkenness
Categorical

CONSTANT 

Distinct1
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size886.0 B
0
13 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13
100.0%

Length

2024-03-03T00:24:39.882434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T00:24:39.962688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13
100.0%

Most occurring characters

ValueCountFrequency (%)
0 13
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13
100.0%

data__Embezzlement
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.769231
Minimum16
Maximum156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:40.004435image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile19.6
Q137
median64
Q3101
95-th percentile133.2
Maximum156
Range140
Interquartile range (IQR)64

Descriptive statistics

Standard deviation42.085536
Coefficient of variation (CV)0.60321054
Kurtosis-0.33410706
Mean69.769231
Median Absolute Deviation (MAD)37
Skewness0.56716993
Sum907
Variance1771.1923
MonotonicityNot monotonic
2024-03-03T00:24:40.072449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
156 1
 
7.7%
103 1
 
7.7%
118 1
 
7.7%
101 1
 
7.7%
90 1
 
7.7%
52 1
 
7.7%
74 1
 
7.7%
64 1
 
7.7%
48 1
 
7.7%
37 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
16 1
7.7%
22 1
7.7%
26 1
7.7%
37 1
7.7%
48 1
7.7%
52 1
7.7%
64 1
7.7%
74 1
7.7%
90 1
7.7%
101 1
7.7%
ValueCountFrequency (%)
156 1
7.7%
118 1
7.7%
103 1
7.7%
101 1
7.7%
90 1
7.7%
74 1
7.7%
64 1
7.7%
52 1
7.7%
48 1
7.7%
37 1
7.7%

data__Forgery and Counterfeiting
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2575.6154
Minimum257
Maximum3647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:40.136107image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum257
5-th percentile947.6
Q12207
median2795
Q33122
95-th percentile3630.8
Maximum3647
Range3390
Interquartile range (IQR)915

Descriptive statistics

Standard deviation950.62055
Coefficient of variation (CV)0.36908482
Kurtosis1.8064115
Mean2575.6154
Median Absolute Deviation (MAD)408
Skewness-1.3269527
Sum33483
Variance903679.42
MonotonicityNot monotonic
2024-03-03T00:24:40.229450image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3620 1
 
7.7%
3203 1
 
7.7%
3115 1
 
7.7%
3647 1
 
7.7%
3122 1
 
7.7%
2713 1
 
7.7%
2795 1
 
7.7%
2852 1
 
7.7%
2730 1
 
7.7%
2207 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
257 1
7.7%
1408 1
7.7%
1814 1
7.7%
2207 1
7.7%
2713 1
7.7%
2730 1
7.7%
2795 1
7.7%
2852 1
7.7%
3115 1
7.7%
3122 1
7.7%
ValueCountFrequency (%)
3647 1
7.7%
3620 1
7.7%
3203 1
7.7%
3122 1
7.7%
3115 1
7.7%
2852 1
7.7%
2795 1
7.7%
2730 1
7.7%
2713 1
7.7%
2207 1
7.7%

data__Fraud
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4778.8462
Minimum321
Maximum7868
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:40.309361image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum321
5-th percentile1090.2
Q14142
median5045
Q36234
95-th percentile7157.6
Maximum7868
Range7547
Interquartile range (IQR)2092

Descriptive statistics

Standard deviation2078.9153
Coefficient of variation (CV)0.43502453
Kurtosis0.64499093
Mean4778.8462
Median Absolute Deviation (MAD)1189
Skewness-0.83124178
Sum62125
Variance4321888.8
MonotonicityStrictly decreasing
2024-03-03T00:24:40.372709image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
7868 1
 
7.7%
6684 1
 
7.7%
6607 1
 
7.7%
6234 1
 
7.7%
5720 1
 
7.7%
5378 1
 
7.7%
5045 1
 
7.7%
4481 1
 
7.7%
4417 1
 
7.7%
4142 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
321 1
7.7%
1603 1
7.7%
3625 1
7.7%
4142 1
7.7%
4417 1
7.7%
4481 1
7.7%
5045 1
7.7%
5378 1
7.7%
5720 1
7.7%
6234 1
7.7%
ValueCountFrequency (%)
7868 1
7.7%
6684 1
7.7%
6607 1
7.7%
6234 1
7.7%
5720 1
7.7%
5378 1
7.7%
5045 1
7.7%
4481 1
7.7%
4417 1
7.7%
4142 1
7.7%

data__Gambling - Total
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.69231
Minimum7
Maximum237
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:40.456476image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile22.6
Q145
median82
Q3163
95-th percentile210.6
Maximum237
Range230
Interquartile range (IQR)118

Descriptive statistics

Standard deviation71.426401
Coefficient of variation (CV)0.68225071
Kurtosis-0.83000297
Mean104.69231
Median Absolute Deviation (MAD)45
Skewness0.52540561
Sum1361
Variance5101.7308
MonotonicityNot monotonic
2024-03-03T00:24:40.515756image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
237 1
 
7.7%
163 1
 
7.7%
193 1
 
7.7%
190 1
 
7.7%
120 1
 
7.7%
82 1
 
7.7%
78 1
 
7.7%
74 1
 
7.7%
102 1
 
7.7%
45 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
7 1
7.7%
33 1
7.7%
37 1
7.7%
45 1
7.7%
74 1
7.7%
78 1
7.7%
82 1
7.7%
102 1
7.7%
120 1
7.7%
163 1
7.7%
ValueCountFrequency (%)
237 1
7.7%
193 1
7.7%
190 1
7.7%
163 1
7.7%
120 1
7.7%
102 1
7.7%
82 1
7.7%
78 1
7.7%
74 1
7.7%
45 1
7.7%

data__Human Trafficking - Commercial Sex Acts
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size886.0 B
0
12 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)7.7%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12
92.3%
1 1
 
7.7%

Length

2024-03-03T00:24:40.593979image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T00:24:40.688229image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 12
92.3%
1 1
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 12
92.3%
1 1
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12
92.3%
1 1
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common 13
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12
92.3%
1 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12
92.3%
1 1
 
7.7%
Distinct1
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size886.0 B
0
13 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13
100.0%

Length

2024-03-03T00:24:40.782985image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T00:24:40.848261image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13
100.0%

Most occurring characters

ValueCountFrequency (%)
0 13
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13
100.0%

data__Larceny - Theft
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40991.077
Minimum7789
Maximum51906
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:40.905675image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum7789
5-th percentile18787
Q136890
median45308
Q350330
95-th percentile51434.4
Maximum51906
Range44117
Interquartile range (IQR)13440

Descriptive statistics

Standard deviation12690.661
Coefficient of variation (CV)0.3095957
Kurtosis3.0661717
Mean40991.077
Median Absolute Deviation (MAD)5812
Skewness-1.6974991
Sum532884
Variance1.6105288 × 108
MonotonicityNot monotonic
2024-03-03T00:24:40.957269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
49523 1
 
7.7%
50330 1
 
7.7%
51120 1
 
7.7%
51906 1
 
7.7%
50765 1
 
7.7%
47495 1
 
7.7%
45308 1
 
7.7%
42550 1
 
7.7%
39002 1
 
7.7%
36890 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
7789 1
7.7%
26119 1
7.7%
34087 1
7.7%
36890 1
7.7%
39002 1
7.7%
42550 1
7.7%
45308 1
7.7%
47495 1
7.7%
49523 1
7.7%
50330 1
7.7%
ValueCountFrequency (%)
51906 1
7.7%
51120 1
7.7%
50765 1
7.7%
50330 1
7.7%
49523 1
7.7%
47495 1
7.7%
45308 1
7.7%
42550 1
7.7%
39002 1
7.7%
36890 1
7.7%

data__Liquor Laws
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2488
Minimum171
Maximum5260
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:41.021747image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum171
5-th percentile223.8
Q11106
median1873
Q34556
95-th percentile5068
Maximum5260
Range5089
Interquartile range (IQR)3450

Descriptive statistics

Standard deviation1845.4444
Coefficient of variation (CV)0.74173812
Kurtosis-1.4656726
Mean2488
Median Absolute Deviation (MAD)1272
Skewness0.35959206
Sum32344
Variance3405665.2
MonotonicityNot monotonic
2024-03-03T00:24:41.084122image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
5260 1
 
7.7%
4726 1
 
7.7%
4556 1
 
7.7%
4940 1
 
7.7%
3145 1
 
7.7%
2467 1
 
7.7%
1873 1
 
7.7%
1609 1
 
7.7%
1498 1
 
7.7%
1106 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
171 1
7.7%
259 1
7.7%
734 1
7.7%
1106 1
7.7%
1498 1
7.7%
1609 1
7.7%
1873 1
7.7%
2467 1
7.7%
3145 1
7.7%
4556 1
7.7%
ValueCountFrequency (%)
5260 1
7.7%
4940 1
7.7%
4726 1
7.7%
4556 1
7.7%
3145 1
7.7%
2467 1
7.7%
1873 1
7.7%
1609 1
7.7%
1498 1
7.7%
1106 1
7.7%

data__Manslaughter by Negligence
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.076923
Minimum5
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:41.152539image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.8
Q111
median15
Q318
95-th percentile23.8
Maximum28
Range23
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.0340912
Coefficient of variation (CV)0.40022033
Kurtosis0.63001131
Mean15.076923
Median Absolute Deviation (MAD)4
Skewness0.48240628
Sum196
Variance36.410256
MonotonicityNot monotonic
2024-03-03T00:24:41.258482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
15 2
15.4%
21 2
15.4%
11 2
15.4%
28 1
7.7%
16 1
7.7%
14 1
7.7%
18 1
7.7%
13 1
7.7%
8 1
7.7%
5 1
7.7%
ValueCountFrequency (%)
5 1
7.7%
8 1
7.7%
11 2
15.4%
13 1
7.7%
14 1
7.7%
15 2
15.4%
16 1
7.7%
18 1
7.7%
21 2
15.4%
28 1
7.7%
ValueCountFrequency (%)
28 1
7.7%
21 2
15.4%
18 1
7.7%
16 1
7.7%
15 2
15.4%
14 1
7.7%
13 1
7.7%
11 2
15.4%
8 1
7.7%
5 1
7.7%

data__Motor Vehicle Theft
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1836.5385
Minimum760
Maximum2208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:41.336613image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum760
5-th percentile1370.8
Q11811
median1856
Q31986
95-th percentile2174.4
Maximum2208
Range1448
Interquartile range (IQR)175

Descriptive statistics

Standard deviation352.73045
Coefficient of variation (CV)0.19206265
Kurtosis8.3275155
Mean1836.5385
Median Absolute Deviation (MAD)61
Skewness-2.5753615
Sum23875
Variance124418.77
MonotonicityNot monotonic
2024-03-03T00:24:41.415669image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2208 1
 
7.7%
2075 1
 
7.7%
1986 1
 
7.7%
2152 1
 
7.7%
1825 1
 
7.7%
1778 1
 
7.7%
1811 1
 
7.7%
1856 1
 
7.7%
1812 1
 
7.7%
1901 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
760 1
7.7%
1778 1
7.7%
1795 1
7.7%
1811 1
7.7%
1812 1
7.7%
1825 1
7.7%
1856 1
7.7%
1901 1
7.7%
1916 1
7.7%
1986 1
7.7%
ValueCountFrequency (%)
2208 1
7.7%
2152 1
7.7%
2075 1
7.7%
1986 1
7.7%
1916 1
7.7%
1901 1
7.7%
1856 1
7.7%
1825 1
7.7%
1812 1
7.7%
1811 1
7.7%

data__Murder and Nonnegligent Manslaughter
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean242.76923
Minimum94
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:41.478213image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum94
5-th percentile157.6
Q1216
median263
Q3274
95-th percentile284.8
Maximum289
Range195
Interquartile range (IQR)58

Descriptive statistics

Standard deviation53.305963
Coefficient of variation (CV)0.21957462
Kurtosis4.7133851
Mean242.76923
Median Absolute Deviation (MAD)16
Skewness-2.038241
Sum3156
Variance2841.5256
MonotonicityNot monotonic
2024-03-03T00:24:41.549875image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
263 2
15.4%
282 1
7.7%
289 1
7.7%
247 1
7.7%
209 1
7.7%
273 1
7.7%
216 1
7.7%
268 1
7.7%
274 1
7.7%
200 1
7.7%
Other values (2) 2
15.4%
ValueCountFrequency (%)
94 1
7.7%
200 1
7.7%
209 1
7.7%
216 1
7.7%
247 1
7.7%
263 2
15.4%
268 1
7.7%
273 1
7.7%
274 1
7.7%
278 1
7.7%
ValueCountFrequency (%)
289 1
7.7%
282 1
7.7%
278 1
7.7%
274 1
7.7%
273 1
7.7%
268 1
7.7%
263 2
15.4%
247 1
7.7%
216 1
7.7%
209 1
7.7%

data__Offenses Against the Family and Children
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean554.23077
Minimum41
Maximum1324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:41.606998image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile210.8
Q1467
median551
Q3604
95-th percentile910.6
Maximum1324
Range1283
Interquartile range (IQR)137

Descriptive statistics

Standard deviation282.42821
Coefficient of variation (CV)0.50958594
Kurtosis5.2620431
Mean554.23077
Median Absolute Deviation (MAD)60
Skewness1.3335614
Sum7205
Variance79765.692
MonotonicityNot monotonic
2024-03-03T00:24:41.702051image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1324 1
 
7.7%
635 1
 
7.7%
602 1
 
7.7%
467 1
 
7.7%
396 1
 
7.7%
551 1
 
7.7%
604 1
 
7.7%
611 1
 
7.7%
576 1
 
7.7%
536 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
41 1
7.7%
324 1
7.7%
396 1
7.7%
467 1
7.7%
536 1
7.7%
538 1
7.7%
551 1
7.7%
576 1
7.7%
602 1
7.7%
604 1
7.7%
ValueCountFrequency (%)
1324 1
7.7%
635 1
7.7%
611 1
7.7%
604 1
7.7%
602 1
7.7%
576 1
7.7%
551 1
7.7%
538 1
7.7%
536 1
7.7%
467 1
7.7%

data__Prostitution and Commercialized Vice
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean701.84615
Minimum14
Maximum1181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:41.762285image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile112.4
Q1599
median805
Q3896
95-th percentile1050.2
Maximum1181
Range1167
Interquartile range (IQR)297

Descriptive statistics

Standard deviation323.48953
Coefficient of variation (CV)0.4609123
Kurtosis0.65374813
Mean701.84615
Median Absolute Deviation (MAD)158
Skewness-0.93615998
Sum9124
Variance104645.47
MonotonicityNot monotonic
2024-03-03T00:24:41.825374image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
874 1
 
7.7%
896 1
 
7.7%
963 1
 
7.7%
805 1
 
7.7%
1181 1
 
7.7%
923 1
 
7.7%
869 1
 
7.7%
632 1
 
7.7%
688 1
 
7.7%
599 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
14 1
7.7%
178 1
7.7%
502 1
7.7%
599 1
7.7%
632 1
7.7%
688 1
7.7%
805 1
7.7%
869 1
7.7%
874 1
7.7%
896 1
7.7%
ValueCountFrequency (%)
1181 1
7.7%
963 1
7.7%
923 1
7.7%
896 1
7.7%
874 1
7.7%
869 1
7.7%
805 1
7.7%
688 1
7.7%
632 1
7.7%
599 1
7.7%

data__Rape
Real number (ℝ)

UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean699.61538
Minimum205
Maximum1164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:41.907393image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum205
5-th percentile304
Q1469
median534
Q31015
95-th percentile1138.8
Maximum1164
Range959
Interquartile range (IQR)546

Descriptive statistics

Standard deviation324.37826
Coefficient of variation (CV)0.46365227
Kurtosis-1.579177
Mean699.61538
Median Absolute Deviation (MAD)222
Skewness0.17208172
Sum9095
Variance105221.26
MonotonicityNot monotonic
2024-03-03T00:24:41.967325image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
534 1
 
7.7%
516 1
 
7.7%
470 1
 
7.7%
461 1
 
7.7%
370 1
 
7.7%
469 1
 
7.7%
1164 1
 
7.7%
1122 1
 
7.7%
1015 1
 
7.7%
994 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
205 1
7.7%
370 1
7.7%
461 1
7.7%
469 1
7.7%
470 1
7.7%
516 1
7.7%
534 1
7.7%
756 1
7.7%
994 1
7.7%
1015 1
7.7%
ValueCountFrequency (%)
1164 1
7.7%
1122 1
7.7%
1019 1
7.7%
1015 1
7.7%
994 1
7.7%
756 1
7.7%
534 1
7.7%
516 1
7.7%
470 1
7.7%
469 1
7.7%

data__Robbery
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3171
Minimum621
Maximum4380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:42.034602image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum621
5-th percentile1395
Q12798
median3614
Q33820
95-th percentile4104
Maximum4380
Range3759
Interquartile range (IQR)1022

Descriptive statistics

Standard deviation1031.548
Coefficient of variation (CV)0.32530685
Kurtosis1.9816078
Mean3171
Median Absolute Deviation (MAD)306
Skewness-1.4443955
Sum41223
Variance1064091.3
MonotonicityNot monotonic
2024-03-03T00:24:42.094679image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
4380 1
 
7.7%
3854 1
 
7.7%
3920 1
 
7.7%
3820 1
 
7.7%
3799 1
 
7.7%
3614 1
 
7.7%
3646 1
 
7.7%
3337 1
 
7.7%
3236 1
 
7.7%
2798 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
621 1
7.7%
1911 1
7.7%
2287 1
7.7%
2798 1
7.7%
3236 1
7.7%
3337 1
7.7%
3614 1
7.7%
3646 1
7.7%
3799 1
7.7%
3820 1
7.7%
ValueCountFrequency (%)
4380 1
7.7%
3920 1
7.7%
3854 1
7.7%
3820 1
7.7%
3799 1
7.7%
3646 1
7.7%
3614 1
7.7%
3337 1
7.7%
3236 1
7.7%
2798 1
7.7%

data__Simple Assault
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30492.077
Minimum7830
Maximum39851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:42.174926image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum7830
5-th percentile15252
Q128321
median31396
Q338118
95-th percentile39260
Maximum39851
Range32021
Interquartile range (IQR)9797

Descriptive statistics

Standard deviation9014.1134
Coefficient of variation (CV)0.2956215
Kurtosis2.2761414
Mean30492.077
Median Absolute Deviation (MAD)6126
Skewness-1.3842831
Sum396397
Variance81254241
MonotonicityNot monotonic
2024-03-03T00:24:42.379300image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
37004 1
 
7.7%
39851 1
 
7.7%
38866 1
 
7.7%
38118 1
 
7.7%
38197 1
 
7.7%
32185 1
 
7.7%
31396 1
 
7.7%
30120 1
 
7.7%
29039 1
 
7.7%
28321 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
7830 1
7.7%
20200 1
7.7%
25270 1
7.7%
28321 1
7.7%
29039 1
7.7%
30120 1
7.7%
31396 1
7.7%
32185 1
7.7%
37004 1
7.7%
38118 1
7.7%
ValueCountFrequency (%)
39851 1
7.7%
38866 1
7.7%
38197 1
7.7%
38118 1
7.7%
37004 1
7.7%
32185 1
7.7%
31396 1
7.7%
30120 1
7.7%
29039 1
7.7%
28321 1
7.7%

data__Stolen Property: Buying, Receiving, Possessing
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3856.9231
Minimum560
Maximum5854
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:42.440477image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum560
5-th percentile1624.4
Q13006
median3756
Q35301
95-th percentile5668
Maximum5854
Range5294
Interquartile range (IQR)2295

Descriptive statistics

Standard deviation1541.1508
Coefficient of variation (CV)0.39958038
Kurtosis0.043856183
Mean3856.9231
Median Absolute Deviation (MAD)1289
Skewness-0.60727735
Sum50140
Variance2375145.7
MonotonicityNot monotonic
2024-03-03T00:24:42.557439image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
5544 1
 
7.7%
5301 1
 
7.7%
5382 1
 
7.7%
5854 1
 
7.7%
4814 1
 
7.7%
4227 1
 
7.7%
3756 1
 
7.7%
3527 1
 
7.7%
3368 1
 
7.7%
3006 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
560 1
7.7%
2334 1
7.7%
2467 1
7.7%
3006 1
7.7%
3368 1
7.7%
3527 1
7.7%
3756 1
7.7%
4227 1
7.7%
4814 1
7.7%
5301 1
7.7%
ValueCountFrequency (%)
5854 1
7.7%
5544 1
7.7%
5382 1
7.7%
5301 1
7.7%
4814 1
7.7%
4227 1
7.7%
3756 1
7.7%
3527 1
7.7%
3368 1
7.7%
3006 1
7.7%

data__Suspicion
Categorical

CONSTANT 

Distinct1
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size886.0 B
0
13 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13
100.0%

Length

2024-03-03T00:24:42.632947image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T00:24:42.693469image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 13
100.0%

Most occurring characters

ValueCountFrequency (%)
0 13
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13
100.0%

data__Vagrancy
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean663.61538
Minimum16
Maximum1180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:42.770907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile94
Q1593
median755
Q3797
95-th percentile1002.4
Maximum1180
Range1164
Interquartile range (IQR)204

Descriptive statistics

Standard deviation301.20772
Coefficient of variation (CV)0.45388899
Kurtosis1.4711569
Mean663.61538
Median Absolute Deviation (MAD)126
Skewness-0.91878409
Sum8627
Variance90726.09
MonotonicityNot monotonic
2024-03-03T00:24:42.896865image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1180 1
 
7.7%
884 1
 
7.7%
766 1
 
7.7%
807 1
 
7.7%
755 1
 
7.7%
797 1
 
7.7%
731 1
 
7.7%
566 1
 
7.7%
629 1
 
7.7%
593 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
16 1
7.7%
146 1
7.7%
566 1
7.7%
593 1
7.7%
629 1
7.7%
731 1
7.7%
755 1
7.7%
757 1
7.7%
766 1
7.7%
797 1
7.7%
ValueCountFrequency (%)
1180 1
7.7%
884 1
7.7%
807 1
7.7%
797 1
7.7%
766 1
7.7%
757 1
7.7%
755 1
7.7%
731 1
7.7%
629 1
7.7%
593 1
7.7%

data__Vandalism
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14250.769
Minimum4358
Maximum18184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:42.992139image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum4358
5-th percentile8387
Q113410
median15000
Q316457
95-th percentile17639.8
Maximum18184
Range13826
Interquartile range (IQR)3047

Descriptive statistics

Standard deviation3577.4949
Coefficient of variation (CV)0.25103872
Kurtosis4.5853093
Mean14250.769
Median Absolute Deviation (MAD)1590
Skewness-1.8929406
Sum185260
Variance12798470
MonotonicityNot monotonic
2024-03-03T00:24:43.093390image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
16457 1
 
7.7%
17277 1
 
7.7%
17130 1
 
7.7%
18184 1
 
7.7%
15673 1
 
7.7%
15000 1
 
7.7%
14915 1
 
7.7%
15042 1
 
7.7%
14351 1
 
7.7%
13410 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
4358 1
7.7%
11073 1
7.7%
12390 1
7.7%
13410 1
7.7%
14351 1
7.7%
14915 1
7.7%
15000 1
7.7%
15042 1
7.7%
15673 1
7.7%
16457 1
7.7%
ValueCountFrequency (%)
18184 1
7.7%
17277 1
7.7%
17130 1
7.7%
16457 1
7.7%
15673 1
7.7%
15042 1
7.7%
15000 1
7.7%
14915 1
7.7%
14351 1
7.7%
13410 1
7.7%

data__Weapons: Carrying, Possessing, Etc.
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3397.3077
Minimum1152
Maximum4263
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:43.180997image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1152
5-th percentile2160
Q13354
median3580
Q33769
95-th percentile4156.8
Maximum4263
Range3111
Interquartile range (IQR)415

Descriptive statistics

Standard deviation790.94378
Coefficient of variation (CV)0.23281488
Kurtosis5.4187161
Mean3397.3077
Median Absolute Deviation (MAD)226
Skewness-2.0585142
Sum44165
Variance625592.06
MonotonicityNot monotonic
2024-03-03T00:24:43.244205image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
4263 1
 
7.7%
4001 1
 
7.7%
3769 1
 
7.7%
4086 1
 
7.7%
3580 1
 
7.7%
3437 1
 
7.7%
3535 1
 
7.7%
3677 1
 
7.7%
3583 1
 
7.7%
3354 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
1152 1
7.7%
2832 1
7.7%
2896 1
7.7%
3354 1
7.7%
3437 1
7.7%
3535 1
7.7%
3580 1
7.7%
3583 1
7.7%
3677 1
7.7%
3769 1
7.7%
ValueCountFrequency (%)
4263 1
7.7%
4086 1
7.7%
4001 1
7.7%
3769 1
7.7%
3677 1
7.7%
3583 1
7.7%
3580 1
7.7%
3535 1
7.7%
3437 1
7.7%
3354 1
7.7%
Distinct13
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2341.6154
Minimum278
Maximum4208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2024-03-03T00:24:43.322501image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile827.6
Q11787
median2011
Q33502
95-th percentile3954.8
Maximum4208
Range3930
Interquartile range (IQR)1715

Descriptive statistics

Standard deviation1129.5676
Coefficient of variation (CV)0.48238818
Kurtosis-0.45541944
Mean2341.6154
Median Absolute Deviation (MAD)382
Skewness0.13802326
Sum30441
Variance1275922.9
MonotonicityNot monotonic
2024-03-03T00:24:43.417445image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
4208 1
 
7.7%
3786 1
 
7.7%
3502 1
 
7.7%
3588 1
 
7.7%
2393 1
 
7.7%
2159 1
 
7.7%
2007 1
 
7.7%
2011 1
 
7.7%
1787 1
 
7.7%
1894 1
 
7.7%
Other values (3) 3
23.1%
ValueCountFrequency (%)
278 1
7.7%
1194 1
7.7%
1634 1
7.7%
1787 1
7.7%
1894 1
7.7%
2007 1
7.7%
2011 1
7.7%
2159 1
7.7%
2393 1
7.7%
3502 1
7.7%
ValueCountFrequency (%)
4208 1
7.7%
3786 1
7.7%
3588 1
7.7%
3502 1
7.7%
2393 1
7.7%
2159 1
7.7%
2011 1
7.7%
2007 1
7.7%
1894 1
7.7%
1787 1
7.7%

Interactions

2024-03-03T00:24:35.244312image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:46.205208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:47.825382image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:49.750227image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:51.609772image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:53.465888image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:55.203305image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:57.099992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:58.907152image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:00.840024image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:02.617963image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:04.579852image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:06.492011image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:08.455442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:10.486600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:12.637792image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:14.264005image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:16.132638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:17.915163image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:19.957824image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:21.677989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:23.491539image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:25.480329image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:27.405342image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:29.198720image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:31.005305image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:32.900735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:35.335223image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:46.294050image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:47.883267image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:49.821105image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:51.665377image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:53.539849image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:55.264897image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:57.163861image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:58.966910image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:00.921293image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:02.674600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:04.654412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:06.573875image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:08.513456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:10.558378image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:12.734444image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:14.332596image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:16.198591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:17.998764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:20.020008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:21.720526image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:23.557565image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:25.541390image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:27.456173image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:29.239594image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:31.070394image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:32.966633image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:35.400009image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:46.371734image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:47.945744image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:49.895422image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:51.734300image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:53.605830image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:55.341204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:57.233089image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:59.033035image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:00.991232image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:02.753558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:04.738000image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:06.638224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:08.578373image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:10.618359image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:12.801286image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:14.399460image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:16.252692image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:18.097402image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:20.094357image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:21.793073image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:23.617570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:25.599614image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:27.544467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:29.312825image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:31.135633image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:33.033364image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:35.486127image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:46.431197image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:48.020387image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:49.961853image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:51.799904image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:53.670777image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:55.399184image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:57.335402image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:59.091145image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:01.064129image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:02.812832image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:04.817665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:06.703818image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:08.643997image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:10.678922image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:12.848080image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:14.467523image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:16.316232image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:18.185888image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:20.175059image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:21.858429image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:23.688544image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:25.697662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:27.623125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:29.410292image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:31.192244image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:33.099881image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:35.554378image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:46.479763image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:48.071335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:50.020409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:51.871356image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:53.731736image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:55.465981image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:57.404171image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:59.149527image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:01.132664image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:02.867795image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:04.899589image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:06.767532image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:08.708063image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:10.768418image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:12.914628image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:14.545339image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:16.382773image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:18.283955image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:20.222986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:21.939999image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:23.781563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:25.801899image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:27.671889image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:29.482147image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:31.266294image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:33.168546image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:35.618669image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:46.537977image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:48.137151image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:50.071036image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:51.937063image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:53.798491image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:55.542915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:57.471841image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:59.223802image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:01.200636image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:02.932376image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:04.970742image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:06.935035image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-03T00:24:10.875368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-03T00:23:50.136992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-03T00:23:47.487130image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:49.320543image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:51.175915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:53.059891image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:54.829334image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:56.701034image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:58.534850image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:00.425433image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:02.167017image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:04.119571image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:06.074535image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:08.059054image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:09.961614image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:12.297071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:13.913512image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:15.765333image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:17.548561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:19.552848image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:21.302899image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:23.151417image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:24.941804image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:27.013074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:28.812454image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:30.656596image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:32.486607image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:34.783500image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:36.783787image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:47.537994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:49.421369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:51.247287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:53.134737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:54.889856image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:56.767245image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:58.583970image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:00.490569image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:02.242178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:04.196411image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:06.148844image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:08.118408image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:10.041129image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:12.347676image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:13.979931image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:15.832109image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:17.608831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:19.619381image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:21.367888image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:23.199940image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:25.062768image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:27.072133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:28.870380image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:30.716287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:32.551039image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:34.864505image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:36.847653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:47.595658image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:49.488570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:51.351221image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:53.183120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:54.950526image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:56.840635image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:58.649526image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:00.556500image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:02.322715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:04.268880image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:06.201339image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:08.174032image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:10.105589image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:12.404926image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:14.030888image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:15.888185image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:17.658038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:19.687997image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:21.420586image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:23.259152image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:25.159558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:27.141453image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:28.922819image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:30.767650image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:32.616764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:34.935828image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:36.904532image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:47.652600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:49.537886image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:51.427760image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:53.251335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:55.006604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:56.905332image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:58.700647image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:00.616366image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:02.424064image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:04.338547image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:06.268249image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:08.236945image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:10.194399image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:12.448092image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:14.084728image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:15.953643image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:17.722161image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:19.753280image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:21.486314image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:23.315297image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:25.232350image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:27.206904image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:28.988945image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:30.826138image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:32.684289image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:35.000561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:36.977657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:47.705989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:49.603641image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:51.486938image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:53.339665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:55.065805image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:56.978032image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:58.773302image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:00.668314image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:02.488304image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:04.412304image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:06.346345image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:08.318602image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:10.284446image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:12.513564image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:14.146796image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:16.021806image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:17.782560image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:19.822279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:21.553653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:23.373962image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:25.297688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:27.256449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:29.062004image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:30.882927image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:32.752735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:35.072937image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:37.044416image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:47.755356image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:49.654474image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:51.537741image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:53.406476image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:55.133509image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:57.035470image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:23:58.829940image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:00.741774image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:02.553144image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:04.499034image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:06.401506image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:08.389925image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:10.372998image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:12.564894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:14.197326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:16.070342image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:17.848613image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:19.889905image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:21.603794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:23.425363image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:25.410818image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:27.322472image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:29.126364image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:30.933816image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:32.828111image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-03T00:24:35.177626image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-03-03T00:24:43.511594image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
data__Aggravated Assaultdata__All Other Offenses (Except Traffic)data__Arsondata__Burglarydata__Disorderly Conductdata__Driving Under the Influencedata__Drug Abuse Violations - Grand Totaldata__Embezzlementdata__Forgery and Counterfeitingdata__Frauddata__Gambling - Totaldata__Human Trafficking - Commercial Sex Actsdata__Larceny - Theftdata__Liquor Lawsdata__Manslaughter by Negligencedata__Motor Vehicle Theftdata__Murder and Nonnegligent Manslaughterdata__Offenses Against the Family and Childrendata__Prostitution and Commercialized Vicedata__Rapedata__Robberydata__Sex Offenses (Except Rape, and Prostitution and Commercialized Vice)data__Simple Assaultdata__Stolen Property: Buying, Receiving, Possessingdata__Vagrancydata__Vandalismdata__Weapons: Carrying, Possessing, Etc.data__data_year
data__Aggravated Assault1.0000.9560.8540.9070.9560.9230.3350.9730.9780.9560.9230.7980.8740.9560.6150.7360.4510.6650.720-0.1100.9560.9560.8900.9290.7750.9450.967-0.956
data__All Other Offenses (Except Traffic)0.9561.0000.8760.9401.0000.9780.3350.9780.9181.0000.9450.6740.9070.9840.5930.6540.4020.6430.841-0.2200.9890.9840.9400.9560.8680.9340.896-1.000
data__Arson0.8540.8761.0000.8430.8760.8100.5950.8130.8710.8760.9060.6740.8150.9090.5130.6310.4730.4300.725-0.2980.8370.8790.7990.8870.8070.8260.835-0.876
data__Burglary0.9070.9400.8431.0000.9400.9620.3900.9290.9180.9400.9290.6740.9780.9560.4330.6650.2590.4560.813-0.3080.9400.9340.9450.9840.7860.9730.868-0.940
data__Disorderly Conduct0.9561.0000.8760.9401.0000.9780.3350.9780.9181.0000.9450.0000.9070.9840.5930.6540.4020.6430.841-0.2200.9890.9840.9400.9560.8680.9340.896-1.000
data__Driving Under the Influence0.9230.9780.8100.9620.9781.0000.3570.9670.8850.9780.9340.7980.9400.9510.5160.6210.3250.5990.874-0.2420.9780.9510.9730.9450.8240.9450.863-0.978
data__Drug Abuse Violations - Grand Total0.3350.3350.5950.3900.3350.3571.0000.2800.3790.3350.4780.7980.4340.3520.1900.3410.261-0.0050.407-0.1810.3020.3460.4180.3630.3350.3850.368-0.335
data__Embezzlement0.9730.9780.8130.9290.9780.9670.2801.0000.9340.9780.9400.0000.9010.9620.5680.6870.3930.6650.802-0.1320.9950.9560.9180.9450.7910.9290.918-0.978
data__Forgery and Counterfeiting0.9780.9180.8710.9180.9180.8850.3790.9341.0000.9180.9010.7390.9070.9510.6070.7140.3690.5380.698-0.1650.9180.9340.8740.9400.7470.9560.951-0.918
data__Fraud0.9561.0000.8760.9401.0000.9780.3350.9780.9181.0000.9450.6740.9070.9840.5930.6540.4020.6430.841-0.2200.9890.9840.9400.9560.8680.9340.896-1.000
data__Gambling - Total0.9230.9450.9060.9290.9450.9340.4780.9400.9010.9451.0000.6030.8960.9450.4830.6980.4150.5380.797-0.2530.9510.9120.8790.9510.7910.8960.912-0.945
data__Human Trafficking - Commercial Sex Acts0.7980.6740.6740.6740.0000.7980.7980.0000.7390.6740.6031.000-0.463-0.463-0.465-0.463-0.464-0.463-0.463-0.463-0.463-0.463-0.463-0.463-0.463-0.463-0.4630.463
data__Larceny - Theft0.8740.9070.8150.9780.9070.9400.4340.9010.9070.9070.896-0.4631.0000.9290.4910.5660.1710.3740.857-0.3410.9120.9010.9510.9560.7580.9560.819-0.907
data__Liquor Laws0.9560.9840.9090.9560.9840.9510.3520.9620.9510.9840.945-0.4630.9291.0000.5930.6700.3360.5710.797-0.2470.9730.9840.9120.9840.8740.9510.912-0.984
data__Manslaughter by Negligence0.6150.5930.5130.4330.5930.5160.1900.5680.6070.5930.483-0.4650.4910.5931.0000.3060.1860.4830.4860.0220.5540.6290.5160.4940.6570.5520.571-0.593
data__Motor Vehicle Theft0.7360.6540.6310.6650.6540.6210.3410.6870.7140.6540.698-0.4630.5660.6700.3061.0000.5250.3740.280-0.0550.6590.6980.5490.6920.5110.6810.747-0.654
data__Murder and Nonnegligent Manslaughter0.4510.4020.4730.2590.4020.3250.2610.3930.3690.4020.415-0.4640.1710.3360.1860.5251.0000.4730.3000.2450.3800.3630.3360.2920.2120.2590.399-0.402
data__Offenses Against the Family and Children0.6650.6430.4300.4560.6430.599-0.0050.6650.5380.6430.538-0.4630.3740.5710.4830.3740.4731.0000.4290.4780.6430.6040.5050.4840.5380.5220.681-0.643
data__Prostitution and Commercialized Vice0.7200.8410.7250.8130.8410.8740.4070.8020.6980.8410.797-0.4630.8570.7970.4860.2800.3000.4291.000-0.2910.8350.7860.9070.7910.6810.7580.610-0.841
data__Rape-0.110-0.220-0.298-0.308-0.220-0.242-0.181-0.132-0.165-0.220-0.253-0.463-0.341-0.2470.022-0.0550.2450.478-0.2911.000-0.176-0.242-0.291-0.291-0.203-0.247-0.0440.220
data__Robbery0.9560.9890.8370.9400.9890.9780.3020.9950.9180.9890.951-0.4630.9120.9730.5540.6590.3800.6430.835-0.1761.0000.9620.9290.9560.8300.9230.896-0.989
data__Sex Offenses (Except Rape, and Prostitution and Commercialized Vice)0.9560.9840.8790.9340.9840.9510.3460.9560.9340.9840.912-0.4630.9010.9840.6290.6980.3630.6040.786-0.2420.9621.0000.9180.9560.8570.9510.901-0.984
data__Simple Assault0.8900.9400.7990.9450.9400.9730.4180.9180.8740.9400.879-0.4630.9510.9120.5160.5490.3360.5050.907-0.2910.9290.9181.0000.9070.7750.9400.802-0.940
data__Stolen Property: Buying, Receiving, Possessing0.9290.9560.8870.9840.9560.9450.3630.9450.9400.9560.951-0.4630.9560.9840.4940.6920.2920.4840.791-0.2910.9560.9560.9071.0000.8130.9560.896-0.956
data__Vagrancy0.7750.8680.8070.7860.8680.8240.3350.7910.7470.8680.791-0.4630.7580.8740.6570.5110.2120.5380.681-0.2030.8300.8570.7750.8131.0000.7800.731-0.868
data__Vandalism0.9450.9340.8260.9730.9340.9450.3850.9290.9560.9340.896-0.4630.9560.9510.5520.6810.2590.5220.758-0.2470.9230.9510.9400.9560.7801.0000.918-0.934
data__Weapons: Carrying, Possessing, Etc.0.9670.8960.8350.8680.8960.8630.3680.9180.9510.8960.912-0.4630.8190.9120.5710.7470.3990.6810.610-0.0440.8960.9010.8020.8960.7310.9181.000-0.896
data__data_year-0.956-1.000-0.876-0.940-1.000-0.978-0.335-0.978-0.918-1.000-0.9450.463-0.907-0.984-0.593-0.654-0.402-0.643-0.8410.220-0.989-0.984-0.940-0.956-0.868-0.934-0.8961.000

Missing values

2024-03-03T00:24:37.161880image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-03T00:24:37.435666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

titlekeysdata__data_yeardata__Aggravated Assaultdata__All Other Offenses (Except Traffic)data__Arsondata__Burglarydata__Curfew and Loitering Law Violationsdata__Disorderly Conductdata__Driving Under the Influencedata__Drug Abuse Violations - Grand Totaldata__Drunkennessdata__Embezzlementdata__Forgery and Counterfeitingdata__Frauddata__Gambling - Totaldata__Human Trafficking - Commercial Sex Actsdata__Human Trafficking - Involuntary Servitudedata__Larceny - Theftdata__Liquor Lawsdata__Manslaughter by Negligencedata__Motor Vehicle Theftdata__Murder and Nonnegligent Manslaughterdata__Offenses Against the Family and Childrendata__Prostitution and Commercialized Vicedata__Rapedata__Robberydata__Simple Assaultdata__Stolen Property: Buying, Receiving, Possessingdata__Suspiciondata__Vagrancydata__Vandalismdata__Weapons: Carrying, Possessing, Etc.data__Sex Offenses (Except Rape, and Prostitution and Commercialized Vice)
0All Arrest by OffenseAggravated Assault20091069079418419831701506336694619570156362078682370049523526028220828213248745344380370045544011801645742634208
1NaNAll Other Offenses (Except Traffic)201010590746633518747014302387206571001033203668416300503304726162075289635896516385439851530108841727740013786
2NaNArson20119759697032868841013176392176533101183115660719300511204556141986247602963470392038866538207661713037693502
3NaNBurglary201210135681233809333012904340426732201013647623419000519064940152152209467805461382038118585408071818440863588
4NaNCurfew and Loitering Law Violations20139025528613448130010341311746732409031225720120005076531452118252733961181370379938197481407551567335802393
5NaNDisorderly Conduct20148531474762987198087313054664385052271353788200474952467151778216551923469361432185422707971500034372159
6NaNDriving Under the Influence201586984728628264270728730361250130742795504578004530818731118112686048691164364631396375607311491535352007
7NaNDrug Abuse Violations - Grand Total201687654497124259500649829984258170642852448174004255016091818562636116321122333730120352705661504236772011
8NaNDrunkenness2017859444355298548005588285337671604827304417102003900214981318122745766881015323629039336806291435135831787
9NaNEmbezzlement20188146423432824696052712700076108037220741424500368901106111901263536599994279828321300605931341033541894
titlekeysdata__data_yeardata__Aggravated Assaultdata__All Other Offenses (Except Traffic)data__Arsondata__Burglarydata__Curfew and Loitering Law Violationsdata__Disorderly Conductdata__Driving Under the Influencedata__Drug Abuse Violations - Grand Totaldata__Drunkennessdata__Embezzlementdata__Forgery and Counterfeitingdata__Frauddata__Gambling - Totaldata__Human Trafficking - Commercial Sex Actsdata__Human Trafficking - Involuntary Servitudedata__Larceny - Theftdata__Liquor Lawsdata__Manslaughter by Negligencedata__Motor Vehicle Theftdata__Murder and Nonnegligent Manslaughterdata__Offenses Against the Family and Childrendata__Prostitution and Commercialized Vicedata__Rapedata__Robberydata__Simple Assaultdata__Stolen Property: Buying, Receiving, Possessingdata__Suspiciondata__Vagrancydata__Vandalismdata__Weapons: Carrying, Possessing, Etc.data__Sex Offenses (Except Rape, and Prostitution and Commercialized Vice)
3NaNBurglary201210135681233809333012904340426732201013647623419000519064940152152209467805461382038118585408071818440863588
4NaNCurfew and Loitering Law Violations20139025528613448130010341311746732409031225720120005076531452118252733961181370379938197481407551567335802393
5NaNDisorderly Conduct20148531474762987198087313054664385052271353788200474952467151778216551923469361432185422707971500034372159
6NaNDriving Under the Influence201586984728628264270728730361250130742795504578004530818731118112686048691164364631396375607311491535352007
7NaNDrug Abuse Violations - Grand Total201687654497124259500649829984258170642852448174004255016091818562636116321122333730120352705661504236772011
8NaNDrunkenness2017859444355298548005588285337671604827304417102003900214981318122745766881015323629039336806291435135831787
9NaNEmbezzlement20188146423432824696052712700076108037220741424500368901106111901263536599994279828321300605931341033541894
10NaNForgery and Counterfeiting20197622368772264060039832626857667026181436253300340877342117952005385021019228725270233407571239028961634
11NaNFraud202071872564322745580192518284352880221408160337002611925981916278324178756191120200246701461107328321194
12NaNGambling - Total2021252812251114131707014256416001625732171077891715760944114205621783056001643581152278